Game-Based Adaptive Optimization Approach for Multi-Agent Systems
Hao Wang, Zheyuan Ning, Hao Luo, Yuchen Jiang, Mingyi Huo · 2023
In this paper, an adaptive distributed optimization approach is investigated for integrator-type multi-agent systems with unknown time-varying disturbances and unmodeled dynamics in non-cooperative games. In partial information games, each agent is considered as a player and local player can know other players' partial decision knowledge through the network. The propagation of local disturbance in the network and the lack of global information make it difficult for local players to make optimal decisions. Aiming at this problem, a disturbance observer algorithm based on neural network is designed to realize disturbance and unmodeled dynamics estimation and a dynamic average consensus algorithm is given to estimate non-neighbor strategy. Estimates of disturbances and unmodeled dynamics are compensated in the control signal to reduce their influence on group decision making. Combined with the gradient optimization method, an adaptive distributed Nash equilibrium seeking method is realized. The simulation results show the effectiveness of the proposed algorithm.